Empirical Comparison of Publication Bias Tests in Meta-Analysis

Abstract

Background: Decision makers rely on meta-analytic estimates to trade off benefits and harms. Publication bias impairs the validity and generalizability of such estimates. The performance of various statistical tests for publication bias has been largely compared using simulation studies and has not been systematically evaluated in empirical data.

Methods: This study compares seven commonly used publication bias tests (i.e., Begg's rank test, trim-and-fill, Egger's, Tang's, Macaskill's, Deeks', and Peters' regression tests) based on 28,655 meta-analyses available in the Cochrane Library.

Results: Egger's regression test detected publication bias more frequently than other tests (15.7% in meta-analyses of binary outcomes and 13.5% in meta-analyses of non-binary outcomes). The proportion of statistically significant publication bias tests was greater for larger meta-analyses, especially for Begg's rank test and the trim-and-fill method. The agreement among Tang's, Macaskill's, Deeks', and Peters' regression tests for binary outcomes was moderately strong (most κ's were around 0.6). Tang's and Deeks' tests had fairly similar performance (κ > 0.9). The agreement among Begg's rank test, the trim-and-fill method, and Egger's regression test was weak or moderate (κ < 0.5).

Conclusions: Given the relatively low agreement between many publication bias tests, meta-analysts should not rely on a single test and may apply multiple tests with various assumptions. Non-statistical approaches to evaluating publication bias (e.g., searching clinical trials registries, records of drug approving agencies, and scientific conference proceedings) remain essential.

Keywords: Cochrane Library; funnel plot; meta-analysis; publication bias; statistical test.

Figure 1

Flow chart of selecting the meta-analyses with non-binary and binary outcomes from the Cochrane Library.

Figure 2

The P values produced by four publication bias tests for all 10,600 Cochrane meta-analyses with non-binary outcomes. Plus signs (+) indicate P values < 10⁻⁷. The total sample sizes were not reported in 1070 meta-analyses, so Tang’s test was not applicable for them, and panel (d) does not contain their results.

Figure 3

The P values produced by seven publication bias tests for all 18,055 Cochrane meta-analyses with binary outcomes. Plus signs (+) indicate P values < 10⁻⁷.

Figure 4

Proportions of the Cochrane meta-analyses having statistically significant publication bias (P value < 0.1) based on various tests and their 95% confidence intervals. “Any test” implies the proportion of the meta-analyses having statistically significant publication bias detected by at least one test. The label “All” on the horizontal axis represents all the extracted meta-analyses with non-binary (upper panel) or binary (lower panel) outcomes.